Previously we were shown steps for installing Apache Spark, now suddenly why this article is sounding like anti-Spark? Apache Spark has problems including need of dependencies & integrity. That is why here is a list of Apache Spark alternatives to overcome integrity issues. At this moment Apache Spark is one step ahead of its competitors, due to some characteristics like implementation and integration of different and very useful tools (like Spark SQL and MLLib) and the ability to store intermediate data in the RDD’s. Due to this motive, many developers are concentrating on it. But trying to get Apache Spark to meet standards of data consistency and integrity is very difficult in some cases. Apache Spark forces to give more importance to data consistency and hence data integrity becomes lesser important. Needless to mention that Apache Spark also forces the coders to all of those dependencies Apache Spark imports.
Apache Spark Alternatives To Overcome Integrity Issues
Apache Flink is considered as powerful competitor of Apache Spark. Spark is based on resilient distributed datasets (RDDs). Flink is optimized for cyclic or iterative processes by using iterative transformations on collections. Flink is also a strong tool for batch processing. Apache Storm is also a relevant direct competitor, although they are not same but they are often used for the same types of tasks likely scalable near-real-time streaming within the Hadoop ecosystem. Other projects include Apache Apex, Apache Beam, Apache Gearpump, Apache Samza, Apache Kylin etc.
For real-time in-memory processing, Apache Ignite is definitely better option. For fast SQL analytics Apache Drill provides similar performance to Spark SQL. For stream processing Apache Beam can blow away Apache Flink.
If you are considering single implementations on Apache Spark, you can identify some competitors like H2O, Storm is an alternative to Spark streaming, and out there are several like-SQL engine which can be counterposed to SparkSQL.